grouped_ttest: Function to run _t_-test on multiple variables across...

In groupedstats: Grouped Statistical Analyses in a Tidy Way

Description

Function to run t-test on multiple variables across multiple grouping variables.

Usage

grouped_ttest(
  data,
  dep.vars,
  indep.vars,
  grouping.vars,
  paired = FALSE,
  var.equal = FALSE
)

Arguments

data	Dataframe from which variables are to be taken.
dep.vars	List dependent variables for a t-test (y in y ~ x).
indep.vars	List independent variables for a t-test (x in y ~ x).
grouping.vars	List of grouping variables.
paired	A logical indicating whether you want a paired t-test (Default: paired = FALSE; independent t-test, i.e.).
var.equal	A logical variable indicating whether to treat the two variances as being equal. If TRUE, then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used (Default: var.equal = FALSE; Welch's t-test, i.e.).

Value

A tibble dataframe with tidy results from t-test analyses.

Examples

# for reproducibility
set.seed(123)

groupedstats::grouped_ttest(
  data = dplyr::filter(.data = ggplot2::diamonds, color == "E" | color == "J"),
  dep.vars = c(carat, price, depth),
  indep.vars = color,
  grouping.vars = clarity,
  paired = FALSE,
  var.equal = FALSE
)

groupedstats documentation built on May 29, 2021, 1:06 a.m.